Snorkel AI Highlights First Wave of Open Benchmarks Grants Projects

Snorkel AI highlighted the first wave of projects supported through its $3 million Open Benchmarks Grants program, launched in February 2026. The company said the initiative addresses a gap between rapid AI advancement and rigorous performance measurement. Supported projects developed with research institutions and communities include Frontier-Bench, Agents' Last Exam, OSWorld 2.0, Continual Learning Bench, SlopCode Bench, and Terminal-Bench 2.1. Separately from the grants program, Snorkel AI led the development of Senior SWE-Bench with research teams at Princeton University and the University of Wisconsin–Madison.

Open Benchmarks Grants recipients

• ▪The Open Benchmarks Grants program provides selected research teams with funding, expert data development support, research and engineering collaboration, and platform resources.
• ▪Snorkel AI launched the Open Benchmarks Grants program in February 2026 as a $3 million commitment to support open-source datasets, benchmarks, and evaluation research.

AI evaluation challenges

• ▪Fred Sala, a member of the Open Benchmarks Grants steering committee, said the projects address evaluation challenges including complex environments, huge autonomy horizons, and rich, sophisticated outputs.
• ▪Snorkel AI described AI systems as advancing faster than the field's ability to rigorously measure their performance on realistic, consequential work.

Funded benchmark projects

• ▪OSWorld 2.0, developed with XLANG Lab, evaluates computer-use agents on 108 long-horizon workflows across 31 self-hosted web environments and professional desktop applications.
• ▪Frontier-Bench, developed with Laude Institute and the Harbor community, evaluates agents in terminal environments under continuous adversarial review.
• ▪Agents' Last Exam, developed with UC Berkeley RDI and the RDI Foundation, evaluates AI agents on long-horizon, economically valuable professional workflows.
• ▪Terminal-Bench Science is in development with support from Open Benchmarks Grants to extend the Terminal-Bench framework to computational research workflows.
• ▪SlopCode Bench, developed with the University of Wisconsin–Madison, measures how code quality degrades as coding agents repeatedly modify and extend their own solutions.
• ▪Continual Learning Bench, developed with UC Berkeley SkyLab and the University of Wisconsin–Madison, measures whether AI agents improve across sequential, stateful tasks.
• ▪Terminal-Bench 2.1, developed with Stanford University, Laude Institute, and the Harbor community, evaluates AI agents on challenging work in terminal environments.

Senior SWE-Bench development

• ▪Snorkel AI led the development of Senior SWE-Bench with research teams at Princeton University and the University of Wisconsin–Madison.
• ▪Senior SWE-Bench evaluates coding agents on senior-level engineering work, including implementing features, investigating bugs, and following existing codebase conventions.

Program partners

• ▪The Open Benchmarks Grants program was established with support from Hugging Face, Prime Intellect, Together AI, Factory, Harbor, and PyTorch.
• ▪Fred Sala, an assistant professor at the University of Wisconsin–Madison, serves as a member of the Open Benchmarks Grants steering committee.

Application process

• ▪Applications for the Open Benchmarks Grants program remain open and are reviewed on a rolling basis.
• ▪The Open Benchmarks Grants program has received hundreds of applications from researchers, labs, and engineers since its launch in February 2026.

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